Featured, Parameterisation, Tutorials

Structural Overlay Parameters and PLPROC

Using the PLPROC parameter preprocessor supplied with the PEST suite, moveable polylinear and polygonal structural features such as faults and aquitard windows can be inserted

Data Space Inversion

This tutorial introduces data space inversion (DSI). DSI can be used to explore the uncertainties of predictions made by complex models with complicated hydraulic property

intro to pyemu

intro_to_pyemu Intro to pyEMU¶ This notebook provides a quick run through some of the capabilities of pyemu for working with PEST(++). This run through is

Introduction to Regression

  Introduction to Regression¶ This tutorial proves an overview of linear regression. It illustrates fitting a polynomial to noisy data, including the role of SSE

Non-Linear Uncertainty Analysis

In contrast to linear uncertainty analysis, non-linear methods do not suffer from the limitation of assuming a linear relationship between model predictions and model parameters.

Covariance Matrices: The PPCOV Suite

A variance-covariance matrix, often referred to as a covariance matrix, is a square matrix that provides covariances between pairs of elements of a random vector.

Data Worth Analysis

The present tutorial addresses the ability (or otherwise) of yet-ungathered data to reduce the uncertainties of decision-critical predictions using linear analysis utilities from the PEST

Linear Uncertainty Analysis

Linear uncertainty analysis is also known as “first order second moment” (or “FOSM”) analysis. It provides approximate mathematical characterisation of prior predictive probability distributions, and

Calibration – A Simple Model

This is the first in a series of tutorials which demonstrate workflows for parameter estimation and uncertainty analysis with the PEST/PEST++ suites. These are not the only